Broken pipelines, a warehouse that’s too slow, a migration stuck at 80%. Describe the problem, and we’ll match a data engineer who’s already fixed the same kind of issue. They work to an agreed outcome with one contact on your side, so you don’t need a data team to manage them.
These are the jobs companies bring to us most often. Each one has a clear finish line.
Give them access, a goal and one person to ask. They’ll come back with questions only when a decision is yours to make.
If the task turns out harder than it looked, the engineer can bring in an architect or a platform specialist from our side.
92% of our engineers are senior, so the person on your task has seen this kind of problem before.
Five of our engineers hold Anthropic’s Claude Certified Architect credential. If your data task feeds an AI agent or a RAG system, you can bring a certified architect in for the design.
AI-assisted development gets tasks closed up to 50% faster with up to 20% fewer bugs, and a senior reviews every change.
Our processes are ISO 27001-certified, and we follow GDPR and HIPAA practices for sensitive data.
What’s broken or missing, what “done” looks like and which tools you run. We’ll reply within 24 hours.
We put forward engineers whose past work matches your task, so the first call is about your problem, not their resume.
Scope, finish line and how you’ll check the result, all written down before work starts.
You get regular updates on progress, and the engineer only needs your time for decisions and access.
Code, docs and a walkthrough. Then you close the engagement or give the engineer the next task.
One short call is enough to see whether the task fits one engineer and what level it needs.
Yes, it can. Many clients who hire a data engineer don’t have a data team at all, just a CTO, a product lead or a BI analyst who needs the data fixed. The engineer needs three things from you: access to the systems involved, a clear goal and one person who can make decisions.
Without a data team on your side, the engineer also covers what a team would normally handle. They document what they build, set up alerts that reach the right person and explain trade-offs in plain terms, so you can decide without a data background.
Before the first call, write down who that person is on your side and how much time they can give each week. An hour or two is usually enough.
Yes, you can. That’s what this kind of hire is for. You agree on the task and the finish line up front, and the engagement ends when the work is done and handed over.
Good single tasks have a result you can check: a pipeline that runs without failures for a set period, a new source landing in the warehouse every day, queries under a target time or an old system switched off. If you can’t describe the finish line yet, bring the symptoms to the first call, and we’ll help you shape the scope.
If the task is small, the engineer can work on it part-time.
The cost depends on the task more than on anything else:
Describe the task in a few sentences, including what’s broken and what “done” means. Send that when you contact us, and you’ll get an estimate for that task instead of a generic rate.
You get working code in your repository, tests and monitoring, documentation and a handover call where the engineer walks your team or your next developer through the work. Then the engagement ends.
Before you sign off, check the result against the finish line you agreed at the start. If something doesn’t match, raise it before the handover call, while the engineer still has the full context.
If more work shows up, the same engineer can take the next task. If you’d like the engineer to stay long-term and work inside your team, that’s data engineering staff augmentation.
For most tasks, a data engineer is the right choice: pipelines, warehouses, ETL, integrations and migrations. That covers the typical setup, where data comes from SaaS tools and databases and lands in Snowflake, BigQuery or Databricks.
A Big Data engineer makes sense when volume and speed are the problem, for example streaming millions of events, tuning Spark jobs on very large datasets or running Kafka and Flink in production. If that sounds like your case, you can hire big data engineers for it.
Not sure which one fits? Tell us your data volumes and how fresh the data needs to be, and we’ll suggest the right profile.
One engineer works well when the task has a clear scope and someone on your side can make decisions. Fixing a pipeline, connecting a source or tuning a warehouse usually fits one person.
When the work spans several systems, needs an architect, a QA engineer and a project manager, or you’d rather hand over the whole outcome, it makes more sense to order data engineering services as a full project.
You can also start with one engineer and switch later. If the task grows, we’ll suggest adding people or moving to a project format.
You own everything the engineer builds: code, data models, documentation and configs. The IP terms are agreed in the contract before work starts.
The engineer works in your accounts and repositories with the access you grant, and nothing is stored on our side. You can limit access to what the task needs, for example one schema or a staging environment instead of production. When the task is done, you revoke access the same way you would for any contractor.
We sign an NDA before you share any details. Our processes are ISO 27001-certified, and we follow GDPR and HIPAA practices for personal and health data.
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